What is Machine Learning?
Machine Learning is a system that can learn from example through self-improvement and without being explicitly coded by programmer.
Machine learning is closely related to data mining and Bayesian predictive modeling. The machine receives data as input, use an algorithm to formulate answers.
Machine learning is also used for a variety of task like fraud detection, predictive maintenance, portfolio optimization, automatize task and so on.
Machine Learning is a system that can learn from example through self-improvement and without being explicitly coded by programmer.
Machine learning is closely related to data mining and Bayesian predictive modeling. The machine receives data as input, use an algorithm to formulate answers.
Machine learning is also used for a variety of task like fraud detection, predictive maintenance, portfolio optimization, automatize task and so on.
The core objective of machine learning is the learning and inference. First of all, the machine learns through the discovery of patterns. This discovery is made thanks to the data. One crucial part of the data scientist is to choose carefully which data to provide to the machine. The list of attributes used to solve a problem is called a feature vector. You can think of a feature vector as a subset of data that is used to tackle a problem.
The life of Machine Learning programs is straightforward and can be summarized in the following points:
1. Define a question
2. Collect data
3. Visualize data
4. Train algorithm
5. Test the Algorithm
6. Collect feedback
7. Refine the algorithm
8. Loop 4-7 until the results are satisfying
9. Use the model to make a prediction
Once the algorithm gets good at drawing the right conclusions, it applies that knowledge to new sets of data.
The life of Machine Learning programs is straightforward and can be summarized in the following points:
1. Define a question
2. Collect data
3. Visualize data
4. Train algorithm
5. Test the Algorithm
6. Collect feedback
7. Refine the algorithm
8. Loop 4-7 until the results are satisfying
9. Use the model to make a prediction
Once the algorithm gets good at drawing the right conclusions, it applies that knowledge to new sets of data.
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